Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
VAEL: Bridging Variational Autoencoders and Probabilistic Logic Programming
Authors: Eleonora Misino, Giuseppe Marra, Emanuele Sansone
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments provide support on the benefits of this neuro-symbolic integration both in terms of task generalization and data efficiency. |
| Researcher Affiliation | Academia | Eleonora Misino Department of Computer Science and Engineering University of Bologna, Italy EMAIL Giuseppe Marra, Emanuele Sansone Department of Computer Science KU Leuven, Belgium {first}.{last}@kuleuven.be |
| Pseudocode | Yes | In Appendix ?? we report VAEL training algorithm (Algorithm ??) along with further details on the training procedure. |
| Open Source Code | Yes | The source code and the datasets are available at https://github.com/elemisi/vael under MIT license. |
| Open Datasets | Yes | 2digit MNIST dataset. We create a dataset of 64, 400 images of two digits taken from the MNIST dataset [38]... The source code and the datasets are available at https://github.com/elemisi/vael under MIT license. |
| Dataset Splits | Yes | We use 65%, 20%, 15% splits for the train, validation and test sets, respectively. |
| Hardware Specification | No | The paper discusses the training and evaluation of models but does not provide specific details regarding the hardware (e.g., CPU, GPU models, memory) used for the experiments. |
| Software Dependencies | No | The paper mentions tools like Prob Log but does not specify versions for any key software components or libraries required to reproduce the experiments. |
| Experiment Setup | No | The paper states that "Further implementation details can be found in Appendix ??" but does not provide specific hyperparameter values or detailed training configurations within the main text. |